The present invention generally relates to a device and a method for determining an edge histogram of an image, to a device and a method for storing an image in an image database, to a device and a method for finding two similar images and to a computer program.
According to one embodiment, the present invention further relates to a method for generating a texture edge histogram of digital images. According to a further embodiment, the present invention relates to a content-based description of images based on color, texture and edge information with the aim of being able to execute a similarity search and/or a classification of images into semantic groups.
For a number of years, the size of digital image archives has been growing immensely, even gigantically. Newspaper publishing agencies, news agencies, internet communities but also more and more private users are archiving a wealth of images. Along with the size of the archives, the difficulty of finding archived images is also increasing. The problem of searching increases when the images are not described sensibly or sufficiently by key words.
Thus, methods have been developed for describing images based on their visual characteristics and for executing search processes based on these visual characteristics. The visual characteristics of the images are, for example, characterized by color features, texture features, edge features and/or shape features, so-called descriptors. When archiving the images, these descriptors are, for example, extracted automatically and stored in a database. For each descriptor, a distance measure is defined, for example, which indicates the similarity of two images with regard to this descriptor. A challenge or also a problem in the definition of a descriptor is to illustrate and/or map a similarity of two images “perceived” (for example by a human observer) with regard to a visual feature via the distance measure as optimally as possible.
A content-based similarity search with regard to a descriptor is executed, for example, in the following way: a user provides an exemplary image. A search engine reads the corresponding descriptor with regard to this example image from a database and calculates the distance measures to all images in the database via the similarity measure of the descriptor. Images with a low distance (images with a sufficiently low distance which is, for example, smaller than a predetermined threshold value) are output as a result of the search.
Very different methods have been developed to describe images based on their color features, texture features and/or edge features in the form of descriptors. Each of these methods contributes in its own way to finding images based on their similarity. Further, a new standardization project MPEG-7 was initiated in which a visual description of multimedia data via descriptors is defined. However, these descriptors also have flaws.
Thus, an edge histogram descriptor which was developed especially for describing edge frequencies in images shows good results for images in which clearly outlined objects are in contrast to a background. However, the mentioned edge histogram descriptor, described, for example in U.S. Pat. No. 6,807,298 B1, does not show such good results when extensive textures are contained in images, like, e.g., water surfaces or treetops. Such textures are also detected as edges and mixed with clearly visible edges.
Also in the standardization project MPEG-7 descriptors exist which may be used specifically for textures. Thus, for example in WO 01/41071 A1, a so-called “homogenous texture descriptor” is described. The mentioned descriptors are generally based on an analysis of frequencies according to a Fourier transformation which is applied, for example, across the whole image. Such descriptors are very well suited—in accordance with their purpose—when regular patterns, like, e.g., carpet patterns or wallpaper patterns, are to be described. The mentioned descriptors are, however, hardly suitable with photographs.
Other methods use histograms of a gradient angle of each pixel, whereby very good images may be detected with clearly outlined objects. Such descriptions often fail, however, when images contain finely structured areas (like, e.g., water surfaces, fine branches of trees, clouds, etc.).
It is thus to be noted that methods exist which either concentrate on the detection of clear edges or which specifically describe certain fine regular textures.